Overview

Dataset statistics

Number of variables13
Number of observations6999
Missing cells1006
Missing cells (%)1.1%
Duplicate rows493
Duplicate rows (%)7.0%
Total size in memory287.8 KiB
Average record size in memory42.1 B

Variable types

Text1
Numeric8
Categorical4

Alerts

Dataset has 493 (7.0%) duplicate rowsDuplicates
engine is highly overall correlated with max_power and 3 other fieldsHigh correlation
km_driven is highly overall correlated with yearHigh correlation
max_power is highly overall correlated with engine and 3 other fieldsHigh correlation
seats is highly overall correlated with engineHigh correlation
selling_price is highly overall correlated with engine and 4 other fieldsHigh correlation
torque is highly overall correlated with engine and 2 other fieldsHigh correlation
transmission is highly overall correlated with max_power and 1 other fieldsHigh correlation
year is highly overall correlated with km_driven and 1 other fieldsHigh correlation
seller_type is highly imbalanced (51.8%)Imbalance
mileage has 202 (2.9%) missing valuesMissing
engine has 202 (2.9%) missing valuesMissing
max_power has 197 (2.8%) missing valuesMissing
torque has 203 (2.9%) missing valuesMissing
seats has 202 (2.9%) missing valuesMissing

Reproduction

Analysis started2026-01-01 18:21:41.695364
Analysis finished2026-01-01 18:21:44.493631
Duration2.8 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

name
Text

Distinct1924
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
2026-01-01T21:21:44.590288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length54
Median length42
Mean length25.227747
Min length11

Characters and Unicode

Total characters176569
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique804 ?
Unique (%)11.5%

Sample

1st rowMaruti Swift Dzire VDI
2nd rowSkoda Rapid 1.5 TDI Ambition
3rd rowHyundai i20 Sportz Diesel
4th rowMaruti Swift VXI BSIII
5th rowHyundai Xcent 1.2 VTVT E Plus
ValueCountFrequency (%)
maruti2126
 
6.4%
hyundai1197
 
3.6%
swift689
 
2.1%
mahindra666
 
2.0%
bsiv622
 
1.9%
tata611
 
1.8%
diesel583
 
1.8%
1.2505
 
1.5%
vxi485
 
1.5%
plus459
 
1.4%
Other values (828)25224
76.1%
2026-01-01T21:21:44.744991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26169
 
14.8%
a12741
 
7.2%
i11534
 
6.5%
t8812
 
5.0%
r7759
 
4.4%
o7165
 
4.1%
n6573
 
3.7%
e6564
 
3.7%
u5091
 
2.9%
S4755
 
2.7%
Other values (58)79406
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)176569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26169
 
14.8%
a12741
 
7.2%
i11534
 
6.5%
t8812
 
5.0%
r7759
 
4.4%
o7165
 
4.1%
n6573
 
3.7%
e6564
 
3.7%
u5091
 
2.9%
S4755
 
2.7%
Other values (58)79406
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)176569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26169
 
14.8%
a12741
 
7.2%
i11534
 
6.5%
t8812
 
5.0%
r7759
 
4.4%
o7165
 
4.1%
n6573
 
3.7%
e6564
 
3.7%
u5091
 
2.9%
S4755
 
2.7%
Other values (58)79406
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)176569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26169
 
14.8%
a12741
 
7.2%
i11534
 
6.5%
t8812
 
5.0%
r7759
 
4.4%
o7165
 
4.1%
n6573
 
3.7%
e6564
 
3.7%
u5091
 
2.9%
S4755
 
2.7%
Other values (58)79406
45.0%

year
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.8184
Minimum1983
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.8 KiB
2026-01-01T21:21:44.779614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile2006
Q12011
median2015
Q32017
95-th percentile2019
Maximum2020
Range37
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0530948
Coefficient of variation (CV)0.0020126417
Kurtosis1.7672148
Mean2013.8184
Median Absolute Deviation (MAD)3
Skewness-1.0773023
Sum14094715
Variance16.427578
MonotonicityNot monotonic
2026-01-01T21:21:44.820790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2017870
12.4%
2016736
10.5%
2018704
10.1%
2015662
9.5%
2013581
8.3%
2012563
8.0%
2014532
7.6%
2019511
7.3%
2011499
7.1%
2010336
 
4.8%
Other values (19)1005
14.4%
ValueCountFrequency (%)
19831
 
< 0.1%
19911
 
< 0.1%
19943
 
< 0.1%
19951
 
< 0.1%
19963
 
< 0.1%
199710
0.1%
19989
0.1%
199913
0.2%
200021
0.3%
20018
 
0.1%
ValueCountFrequency (%)
202069
 
1.0%
2019511
7.3%
2018704
10.1%
2017870
12.4%
2016736
10.5%
2015662
9.5%
2014532
7.6%
2013581
8.3%
2012563
8.0%
2011499
7.1%

selling_price
Real number (ℝ)

High correlation 

Distinct637
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean639515.2
Minimum29999
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2026-01-01T21:21:44.866251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum29999
5-th percentile110000
Q1254999
median450000
Q3675000
95-th percentile1925000
Maximum10000000
Range9970001
Interquartile range (IQR)420001

Descriptive statistics

Standard deviation808941.91
Coefficient of variation (CV)1.2649299
Kurtosis21.308644
Mean639515.2
Median Absolute Deviation (MAD)200000
Skewness4.2107557
Sum4.4759669 × 109
Variance6.5438702 × 1011
MonotonicityNot monotonic
2026-01-01T21:21:44.914234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000196
 
2.8%
600000181
 
2.6%
450000178
 
2.5%
350000178
 
2.5%
550000175
 
2.5%
650000161
 
2.3%
400000153
 
2.2%
250000153
 
2.2%
500000152
 
2.2%
200000138
 
2.0%
Other values (627)5334
76.2%
ValueCountFrequency (%)
299991
 
< 0.1%
300002
 
< 0.1%
315041
 
< 0.1%
350003
 
< 0.1%
390001
 
< 0.1%
4000012
0.2%
420002
 
< 0.1%
4500016
0.2%
459572
 
< 0.1%
5000015
0.2%
ValueCountFrequency (%)
100000001
 
< 0.1%
72000001
 
< 0.1%
65230001
 
< 0.1%
62230001
 
< 0.1%
60000004
 
0.1%
59230001
 
< 0.1%
58500001
 
< 0.1%
58300002
 
< 0.1%
58000002
 
< 0.1%
550000027
0.4%

km_driven
Real number (ℝ)

High correlation 

Distinct827
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69584.616
Minimum1
Maximum2360457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2026-01-01T21:21:44.958697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9000
Q135000
median60000
Q397000
95-th percentile150000
Maximum2360457
Range2360456
Interquartile range (IQR)62000

Descriptive statistics

Standard deviation57724.002
Coefficient of variation (CV)0.82955121
Kurtosis410.79799
Mean69584.616
Median Absolute Deviation (MAD)30000
Skewness12.069152
Sum4.8702272 × 108
Variance3.3320604 × 109
MonotonicityNot monotonic
2026-01-01T21:21:45.004244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000464
 
6.6%
70000389
 
5.6%
80000387
 
5.5%
60000367
 
5.2%
50000343
 
4.9%
100000306
 
4.4%
90000291
 
4.2%
110000264
 
3.8%
40000260
 
3.7%
30000215
 
3.1%
Other values (817)3713
53.1%
ValueCountFrequency (%)
11
 
< 0.1%
10006
 
0.1%
13001
 
< 0.1%
13034
 
0.1%
15003
 
< 0.1%
16001
 
< 0.1%
16201
 
< 0.1%
200028
0.4%
21181
 
< 0.1%
21361
 
< 0.1%
ValueCountFrequency (%)
23604571
< 0.1%
15000001
< 0.1%
5774141
< 0.1%
5000002
< 0.1%
4750001
< 0.1%
4400001
< 0.1%
4260001
< 0.1%
3800001
< 0.1%
3764121
< 0.1%
3700001
< 0.1%

fuel
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
Diesel
3793 
Petrol
3120 
CNG
 
52
LPG
 
34

Length

Max length6
Median length6
Mean length5.9631376
Min length3

Characters and Unicode

Total characters41736
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowDiesel
4th rowPetrol
5th rowPetrol

Common Values

ValueCountFrequency (%)
Diesel3793
54.2%
Petrol3120
44.6%
CNG52
 
0.7%
LPG34
 
0.5%

Length

2026-01-01T21:21:45.047325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T21:21:45.077148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel3793
54.2%
petrol3120
44.6%
cng52
 
0.7%
lpg34
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e10706
25.7%
l6913
16.6%
D3793
 
9.1%
i3793
 
9.1%
s3793
 
9.1%
P3154
 
7.6%
t3120
 
7.5%
r3120
 
7.5%
o3120
 
7.5%
G86
 
0.2%
Other values (3)138
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)41736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e10706
25.7%
l6913
16.6%
D3793
 
9.1%
i3793
 
9.1%
s3793
 
9.1%
P3154
 
7.6%
t3120
 
7.5%
r3120
 
7.5%
o3120
 
7.5%
G86
 
0.2%
Other values (3)138
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)41736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e10706
25.7%
l6913
16.6%
D3793
 
9.1%
i3793
 
9.1%
s3793
 
9.1%
P3154
 
7.6%
t3120
 
7.5%
r3120
 
7.5%
o3120
 
7.5%
G86
 
0.2%
Other values (3)138
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)41736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e10706
25.7%
l6913
16.6%
D3793
 
9.1%
i3793
 
9.1%
s3793
 
9.1%
P3154
 
7.6%
t3120
 
7.5%
r3120
 
7.5%
o3120
 
7.5%
G86
 
0.2%
Other values (3)138
 
0.3%

seller_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Individual
5826 
Dealer
967 
Trustmark Dealer
 
206

Length

Max length16
Median length10
Mean length9.6239463
Min length6

Characters and Unicode

Total characters67358
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual5826
83.2%
Dealer967
 
13.8%
Trustmark Dealer206
 
2.9%

Length

2026-01-01T21:21:45.109630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T21:21:45.133069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
individual5826
80.9%
dealer1173
 
16.3%
trustmark206
 
2.9%

Most occurring characters

ValueCountFrequency (%)
d11652
17.3%
i11652
17.3%
a7205
10.7%
l6999
10.4%
u6032
9.0%
I5826
8.6%
v5826
8.6%
n5826
8.6%
e2346
 
3.5%
r1585
 
2.4%
Other values (7)2409
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)67358
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d11652
17.3%
i11652
17.3%
a7205
10.7%
l6999
10.4%
u6032
9.0%
I5826
8.6%
v5826
8.6%
n5826
8.6%
e2346
 
3.5%
r1585
 
2.4%
Other values (7)2409
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)67358
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d11652
17.3%
i11652
17.3%
a7205
10.7%
l6999
10.4%
u6032
9.0%
I5826
8.6%
v5826
8.6%
n5826
8.6%
e2346
 
3.5%
r1585
 
2.4%
Other values (7)2409
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)67358
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d11652
17.3%
i11652
17.3%
a7205
10.7%
l6999
10.4%
u6032
9.0%
I5826
8.6%
v5826
8.6%
n5826
8.6%
e2346
 
3.5%
r1585
 
2.4%
Other values (7)2409
 
3.6%

transmission
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Manual
6095 
Automatic
904 

Length

Max length9
Median length6
Mean length6.3874839
Min length6

Characters and Unicode

Total characters44706
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual6095
87.1%
Automatic904
 
12.9%

Length

2026-01-01T21:21:45.164828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T21:21:45.465939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manual6095
87.1%
automatic904
 
12.9%

Most occurring characters

ValueCountFrequency (%)
a13094
29.3%
u6999
15.7%
M6095
13.6%
n6095
13.6%
l6095
13.6%
t1808
 
4.0%
A904
 
2.0%
o904
 
2.0%
m904
 
2.0%
i904
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)44706
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a13094
29.3%
u6999
15.7%
M6095
13.6%
n6095
13.6%
l6095
13.6%
t1808
 
4.0%
A904
 
2.0%
o904
 
2.0%
m904
 
2.0%
i904
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)44706
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a13094
29.3%
u6999
15.7%
M6095
13.6%
n6095
13.6%
l6095
13.6%
t1808
 
4.0%
A904
 
2.0%
o904
 
2.0%
m904
 
2.0%
i904
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)44706
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a13094
29.3%
u6999
15.7%
M6095
13.6%
n6095
13.6%
l6095
13.6%
t1808
 
4.0%
A904
 
2.0%
o904
 
2.0%
m904
 
2.0%
i904
 
2.0%

owner
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
First Owner
4587 
Second Owner
1791 
Third Owner
473 
Fourth & Above Owner
 
144
Test Drive Car
 
4

Length

Max length20
Median length11
Mean length11.442778
Min length11

Characters and Unicode

Total characters80088
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst Owner
2nd rowSecond Owner
3rd rowFirst Owner
4th rowFirst Owner
5th rowFirst Owner

Common Values

ValueCountFrequency (%)
First Owner4587
65.5%
Second Owner1791
 
25.6%
Third Owner473
 
6.8%
Fourth & Above Owner144
 
2.1%
Test Drive Car4
 
0.1%

Length

2026-01-01T21:21:45.495820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-01T21:21:45.522562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
owner6995
49.0%
first4587
32.1%
second1791
 
12.5%
third473
 
3.3%
fourth144
 
1.0%
144
 
1.0%
above144
 
1.0%
test4
 
< 0.1%
drive4
 
< 0.1%
car4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r12207
15.2%
e8938
11.2%
n8786
11.0%
7291
9.1%
O6995
8.7%
w6995
8.7%
i5064
6.3%
t4735
 
5.9%
F4731
 
5.9%
s4591
 
5.7%
Other values (14)9755
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)80088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r12207
15.2%
e8938
11.2%
n8786
11.0%
7291
9.1%
O6995
8.7%
w6995
8.7%
i5064
6.3%
t4735
 
5.9%
F4731
 
5.9%
s4591
 
5.7%
Other values (14)9755
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)80088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r12207
15.2%
e8938
11.2%
n8786
11.0%
7291
9.1%
O6995
8.7%
w6995
8.7%
i5064
6.3%
t4735
 
5.9%
F4731
 
5.9%
s4591
 
5.7%
Other values (14)9755
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)80088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r12207
15.2%
e8938
11.2%
n8786
11.0%
7291
9.1%
O6995
8.7%
w6995
8.7%
i5064
6.3%
t4735
 
5.9%
F4731
 
5.9%
s4591
 
5.7%
Other values (14)9755
12.2%

mileage
Real number (ℝ)

Missing 

Distinct375
Distinct (%)5.5%
Missing202
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean19.426604
Minimum0
Maximum42
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2026-01-01T21:21:45.562318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.99
Q116.799999
median19.299999
Q322.32
95-th percentile25.83
Maximum42
Range42
Interquartile range (IQR)5.5200005

Descriptive statistics

Standard deviation4.045692
Coefficient of variation (CV)0.20825523
Kurtosis0.67758536
Mean19.426604
Median Absolute Deviation (MAD)2.7000008
Skewness-0.15136978
Sum132042.63
Variance16.367624
MonotonicityNot monotonic
2026-01-01T21:21:45.606489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.89999962197
 
2.8%
19.70000076150
 
2.1%
18.60000038139
 
2.0%
21.10000038131
 
1.9%
17116
 
1.7%
15.96000004105
 
1.5%
17.79999924101
 
1.4%
2299
 
1.4%
12.9899997795
 
1.4%
16.1000003890
 
1.3%
Other values (365)5574
79.6%
(Missing)202
 
2.9%
ValueCountFrequency (%)
016
0.2%
94
 
0.1%
9.55
 
0.1%
102
 
< 0.1%
10.100000382
 
< 0.1%
10.514
0.2%
10.710000041
 
< 0.1%
10.752
 
< 0.1%
10.800000191
 
< 0.1%
10.899999624
 
0.1%
ValueCountFrequency (%)
421
 
< 0.1%
33.439998633
 
< 0.1%
331
 
< 0.1%
32.520000461
 
< 0.1%
30.459999082
 
< 0.1%
28.3999996280
1.1%
28.0900001536
0.5%
27.620000845
 
0.1%
27.399999626
 
0.1%
27.3899993949
0.7%

engine
Real number (ℝ)

High correlation  Missing 

Distinct120
Distinct (%)1.8%
Missing202
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean1458.3272
Minimum624
Maximum3604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2026-01-01T21:21:45.648278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile796
Q11197
median1248
Q31582
95-th percentile2499
Maximum3604
Range2980
Interquartile range (IQR)385

Descriptive statistics

Standard deviation501.1839
Coefficient of variation (CV)0.3436704
Kurtosis0.73242486
Mean1458.3272
Median Absolute Deviation (MAD)248
Skewness1.1392183
Sum9912250
Variance251185.3
MonotonicityNot monotonic
2026-01-01T21:21:45.694946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248885
 
12.6%
1197715
 
10.2%
998393
 
5.6%
796375
 
5.4%
2179330
 
4.7%
1498318
 
4.5%
1396264
 
3.8%
1199233
 
3.3%
2494188
 
2.7%
2523171
 
2.4%
Other values (110)2925
41.8%
(Missing)202
 
2.9%
ValueCountFrequency (%)
62416
 
0.2%
7935
 
0.1%
796375
5.4%
79966
 
0.9%
81499
 
1.4%
9092
 
< 0.1%
93631
 
0.4%
99324
 
0.3%
99541
 
0.6%
998393
5.6%
ValueCountFrequency (%)
36045
 
0.1%
34981
 
< 0.1%
31983
 
< 0.1%
29993
 
< 0.1%
29972
 
< 0.1%
299314
0.2%
29879
 
0.1%
298228
0.4%
29679
 
0.1%
295615
0.2%

max_power
Real number (ℝ)

High correlation  Missing 

Distinct313
Distinct (%)4.6%
Missing197
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean91.500023
Minimum0
Maximum400
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2026-01-01T21:21:45.741458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.299999
Q168.050003
median82
Q3102
95-th percentile171.5
Maximum400
Range400
Interquartile range (IQR)33.949997

Descriptive statistics

Standard deviation35.821621
Coefficient of variation (CV)0.39149303
Kurtosis3.891552
Mean91.500023
Median Absolute Deviation (MAD)14.959999
Skewness1.6386213
Sum622383.16
Variance1283.1886
MonotonicityNot monotonic
2026-01-01T21:21:45.789073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74330
 
4.7%
81.80000305193
 
2.8%
88.5189
 
2.7%
67149
 
2.1%
46.29999924139
 
2.0%
62.09999847130
 
1.9%
67.09999847127
 
1.8%
67.04000092126
 
1.8%
88.69999695125
 
1.8%
70117
 
1.7%
Other values (303)5177
74.0%
(Missing)197
 
2.8%
ValueCountFrequency (%)
06
 
0.1%
32.799999242
 
< 0.1%
34.2000007619
 
0.3%
3516
 
0.2%
35.52
 
< 0.1%
3774
1.1%
37.479999548
 
0.1%
37.56
 
0.1%
381
 
< 0.1%
38.400001532
 
< 0.1%
ValueCountFrequency (%)
4001
 
< 0.1%
2821
 
< 0.1%
2805
0.1%
2721
 
< 0.1%
270.89999393
< 0.1%
2651
 
< 0.1%
261.39999396
0.1%
2582
 
< 0.1%
254.80000313
< 0.1%
254.78999331
 
< 0.1%

torque
Real number (ℝ)

High correlation  Missing 

Distinct243
Distinct (%)3.6%
Missing203
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean184.76681
Minimum47.071918
Maximum5001.3916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2026-01-01T21:21:45.836443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47.071918
5-th percentile69
Q1111.8
median170
Q3209
95-th percentile360
Maximum5001.3916
Range4954.3198
Interquartile range (IQR)97.199997

Descriptive statistics

Standard deviation155.15225
Coefficient of variation (CV)0.83971927
Kurtosis316.4653
Mean184.76681
Median Absolute Deviation (MAD)56.25
Skewness12.998838
Sum1255675.3
Variance24072.221
MonotonicityNot monotonic
2026-01-01T21:21:45.883264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200596
 
8.5%
190535
 
7.6%
90353
 
5.0%
113228
 
3.3%
114211
 
3.0%
160148
 
2.1%
400145
 
2.1%
62142
 
2.0%
320130
 
1.9%
110130
 
1.9%
Other values (233)4178
59.7%
(Missing)203
 
2.9%
ValueCountFrequency (%)
47.071918491
 
< 0.1%
5114
 
0.2%
55.897903441
 
< 0.1%
572
 
< 0.1%
58.839900971
 
< 0.1%
5992
1.3%
59.8205642714
 
0.2%
603
 
< 0.1%
62142
2.0%
69116
1.7%
ValueCountFrequency (%)
5001.3916022
 
< 0.1%
2451.6625982
 
< 0.1%
2059.3964847
0.1%
1863.263551
 
< 0.1%
1421.9642339
0.1%
1274.8645023
 
< 0.1%
1127.7647716
0.1%
1078.7314451
 
< 0.1%
7893
 
< 0.1%
6401
 
< 0.1%

seats
Real number (ℝ)

High correlation  Missing 

Distinct9
Distinct (%)0.1%
Missing202
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean5.4190084
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.5 KiB
2026-01-01T21:21:45.917063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum14
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96576655
Coefficient of variation (CV)0.17821832
Kurtosis4.0454431
Mean5.4190084
Median Absolute Deviation (MAD)0
Skewness2.0111742
Sum36833
Variance0.93270504
MonotonicityNot monotonic
2026-01-01T21:21:45.947314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
55393
77.1%
7944
 
13.5%
8208
 
3.0%
4104
 
1.5%
972
 
1.0%
654
 
0.8%
1019
 
0.3%
22
 
< 0.1%
141
 
< 0.1%
(Missing)202
 
2.9%
ValueCountFrequency (%)
22
 
< 0.1%
4104
 
1.5%
55393
77.1%
654
 
0.8%
7944
 
13.5%
8208
 
3.0%
972
 
1.0%
1019
 
0.3%
141
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
1019
 
0.3%
972
 
1.0%
8208
 
3.0%
7944
 
13.5%
654
 
0.8%
55393
77.1%
4104
 
1.5%
22
 
< 0.1%

Interactions

2026-01-01T21:21:44.055126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:41.948481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.282393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.608658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.892831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.169426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.458883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.772536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:44.093118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:41.993433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.324748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.647451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.929252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.206331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.496545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.807849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:44.129365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.035960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.365126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.684216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.963386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.243053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.536455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.843195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:44.164131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.081667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.403367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.717980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.996417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.278323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.578341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.877252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:44.197909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.119409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.442978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.751362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.027734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.312972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.615764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.911055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:44.235073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.159658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.484405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.787427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.066067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.349210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.656681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.947032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:44.271743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.203237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.527062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.824692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.102369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.387147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.698933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.983366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:44.306948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.242097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.569095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:42.858725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.135687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.422749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:43.735255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-01T21:21:44.017604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-01T21:21:45.979321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
enginefuelkm_drivenmax_powermileageownerseatsseller_typeselling_pricetorquetransmissionyear
engine1.0000.4500.2380.736-0.4620.0820.5150.2290.5120.8440.489-0.006
fuel0.4501.0000.0350.1680.3090.0310.2130.1080.1100.0270.0500.122
km_driven0.2380.0351.000-0.036-0.1570.0340.2250.018-0.3580.1800.027-0.620
max_power0.7360.168-0.0361.000-0.3450.0850.2750.2580.6690.7930.5930.220
mileage-0.4620.309-0.157-0.3451.0000.086-0.4390.070-0.031-0.1990.2120.302
owner0.0820.0310.0340.0850.0861.0000.0340.1690.3640.0320.1690.269
seats0.5150.2130.2250.275-0.4390.0341.0000.0640.2610.4020.075-0.013
seller_type0.2290.1080.0180.2580.0700.1690.0641.0000.2820.0000.3730.185
selling_price0.5120.110-0.3580.669-0.0310.3640.2610.2821.0000.6110.5860.714
torque0.8440.0270.1800.793-0.1990.0320.4020.0000.6111.0000.1540.126
transmission0.4890.0500.0270.5930.2120.1690.0750.3730.5860.1541.0000.269
year-0.0060.122-0.6200.2200.3020.269-0.0130.1850.7140.1260.2691.000

Missing values

2026-01-01T21:21:44.365112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-01T21:21:44.411664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-01T21:21:44.462936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats
0Maruti Swift Dzire VDI2014450000145500DieselIndividualManualFirst Owner23.4000001248.074.000000190.0000005.0
1Skoda Rapid 1.5 TDI Ambition2014370000120000DieselIndividualManualSecond Owner21.1399991498.0103.519997250.0000005.0
2Hyundai i20 Sportz Diesel2010225000127000DieselIndividualManualFirst Owner23.0000001396.090.000000219.6689615.0
3Maruti Swift VXI BSIII2007130000120000PetrolIndividualManualFirst Owner16.1000001298.088.199997112.7764745.0
4Hyundai Xcent 1.2 VTVT E Plus201744000045000PetrolIndividualManualFirst Owner20.1399991197.081.860001113.7500005.0
5Maruti Wagon R LXI DUO BSIII200796000175000LPGIndividualManualFirst Owner17.2999991061.057.50000076.4918675.0
6Maruti 800 DX BSII2001450005000PetrolIndividualManualSecond Owner16.100000796.037.00000059.0000004.0
7Toyota Etios VXD201135000090000DieselIndividualManualFirst Owner23.5900001364.067.099998170.0000005.0
8Ford Figo Diesel Celebration Edition2013200000169000DieselIndividualManualFirst Owner20.0000001399.068.099998160.0000005.0
9Renault Duster 110PS Diesel RxL201450000068000DieselIndividualManualSecond Owner19.0100001461.0108.449997248.0000005.0
nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats
6989Maruti Swift Dzire VDI201562500050000DieselIndividualManualFirst Owner26.5900001248.074.000000190.0000005.0
6990Hyundai i20 Magna201338000025000PetrolIndividualManualFirst Owner18.5000001197.082.849998113.6999975.0
6991Maruti Wagon R LXI Optional201736000080000PetrolIndividualManualFirst Owner20.510000998.067.04000190.0000005.0
6992Hyundai Santro Xing GLS2008120000191000PetrolIndividualManualFirst Owner17.9200001086.062.09999896.0999985.0
6993Maruti Wagon R VXI BS IV with ABS201326000050000PetrolIndividualManualSecond Owner18.900000998.067.09999890.0000005.0
6994Hyundai i20 Magna2013320000110000PetrolIndividualManualFirst Owner18.5000001197.082.849998113.6999975.0
6995Hyundai Verna CRDi SX2007135000119000DieselIndividualManualFourth & Above Owner16.7999991493.0110.000000235.3596045.0
6996Maruti Swift Dzire ZDi2009382000120000DieselIndividualManualFirst Owner19.2999991248.073.900002190.0000005.0
6997Tata Indigo CR4201329000025000DieselIndividualManualFirst Owner23.5700001396.070.000000140.0000005.0
6998Tata Indigo CR4201329000025000DieselIndividualManualFirst Owner23.5700001396.070.000000140.0000005.0

Duplicate rows

Most frequently occurring

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats# duplicates
64Honda Amaze V CVT Petrol BSIV20197790007032PetrolTrustmark DealerAutomaticFirst Owner19.0000001199.088.760002110.0000005.030
192Lexus ES 300h2019515000020000PetrolDealerAutomaticFirst Owner22.3700012487.0214.559998202.0000005.030
183Jaguar XF 2.0 Diesel Portfolio2017320000045000DieselDealerAutomaticFirst Owner19.3300001999.0177.000000430.0000005.028
467Toyota Innova 2.5 VX (Diesel) 7 Seater201375000079328DieselTrustmark DealerManualSecond Owner12.9900002494.0100.599998200.0000007.028
13BMW X4 M Sport X xDrive20d201954000007500DieselDealerAutomaticFirst Owner16.7800011995.0190.000000400.0000005.027
255Maruti Baleno Alpha 1.3201874000038817DieselDealerManualFirst Owner27.3899991248.074.000000190.0000005.027
306Maruti Swift AMT ZXI201860000069779PetrolDealerAutomaticFirst Owner22.0000001197.081.800003113.0000005.027
363Maruti Wagon R LXI201322500058343PetrolTrustmark DealerManualFirst Owner21.790001998.067.05000390.0000005.027
455Toyota Etios VX201762500025538PetrolTrustmark DealerManualFirst Owner16.7800011496.088.730003132.0000005.027
114Hyundai Grand i10 1.2 CRDi Sportz201745000056290DieselDealerManualFirst Owner24.0000001186.073.970001190.2400055.026